Leveraging powerful deep learning techniques, the end-to-end (E2E) learning of communication system is able to outperform the classical communication system. Unfortunately, this communication system cannot be trained by deep learning without known channel. To deal with this problem, a generative adversarial network (GAN) based training scheme has been recently proposed to imitate the real channel. However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system. To mitigate these two problems, we propose a residual aided GAN (RA-GAN) based training scheme in this paper. Particularly, inspired by the idea of residual learning, we propose a residual generator to mitigate the gradient vanishing problem by realizing a more robust gradient backpropagation. Moreover, to cope with the overfitting problem, we reconstruct the loss function for training by adding a regularizer, which limits the representation ability of RA-GAN. Simulation results show that the trained residual generator has better generation performance than the conventional generator, and the proposed RA-GAN based training scheme can achieve the near-optimal block error rate (BLER) performance with a negligible computational complexity increase in both the theoretical channel model and the ray-tracing based channel dataset.

中文翻译：

---

无已知信道的通信系统的残差端到端学习

利用强大的深度学习技术，通信系统的端到端（E2E）学习能够超越经典通信系统。不幸的是，在没有已知渠道的情况下，该通信系统无法通过深度学习进行训练。为了解决这个问题，最近提出了一种基于生成对抗网络 (GAN) 的训练方案来模拟真实通道。然而，GAN 的梯度消失和过拟合问题会导致通信系统端到端学习的性能严重下降。为了缓解这两个问题，我们在本文中提出了一种基于残差辅助 GAN（RA-GAN）的训练方案。特别是，受残差学习思想的启发，我们提出了一种残差生成器，通过实现更稳健的梯度反向传播来缓解梯度消失问题。此外，为了应对过拟合问题，我们通过添加正则化器来重构训练损失函数，这限制了 RA-GAN 的表示能力。仿真结果表明，经过训练的残差生成器比传统的生成器具有更好的生成性能，并且所提出的基于 RA-GAN 的训练方案可以在理论通道的计算复杂度增加可忽略不计的情况下实现接近最优的误块率（BLER）性能。模型和基于光线追踪的通道数据集。